Explore the simplicity of building regression models in Azure ML Designer. Learn how to preprocess data seamlessly for accurate regression model training.
Table of Contents
Question
You need to create a new pipeline to train a regression model using Azure ML Designer. You ingest your data for the model and drop it on the canvas.
What module would you typically drag-and-drop next on the canvas?
A. Train Model
B. Normalize Data
C. Select Columns in Dataset
D. Split Data
E. Clean Missing data
Answer
C. Select Columns in Dataset
Explanation
After we bring data for model training or ingest data, the next stage is the Data transformation. Data transformation or data pre-processing usually includes the following steps: feature selection, data cleaning, and data normalization.
In Azure ML Designer, we need to drag-and-drop the “Select Columns in Dataset” module from the Data Transformation section. Then on the right-side panel, we can select all the features we want to use for the model training.
After ingesting data into Azure ML Designer, the subsequent step often involves adding a data preparation module, such as “Normalize Data” or “Scale and Reduce.” These modules assist in preparing and pre-processing the data before training the regression model.
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